The fingerprint identification technology has been widely used in various fields, including crime investigation, physical and logical access control, time and attendance. The key technology of fingerprint identification is to estimate an orientation field of a fingerprint. In practical applications, the collected fingerprint images may have very complicated background textures (such as fingerprint images collected from a crime scene using chemical agents or optical instrument in the crime investigation), or the fingerprint images may have poor quality of the fingerprint ridge.
In the prior art, the initial orientation field of the fingerprint can be estimated according to local characteristics of the fingerprint image, and then can be optimized by the smoothness constraint of the fingerprint ridge. Such method has a good performance when the quality of the fingerprint image is high and the background of the fingerprint image is clean enough. However, such method does not work when the fingerprint image has a strong interference of the background texture or has a poor image quality.
As it is unable to ensure the accuracy of the initial orientation field, it is difficult to enhance and match with the initial orientation field of the fingerprint, and totally misleading results will be generated. In this case, the characteristics of the fingerprint image should be manually extracted, and the extracted characteristics should be manually matched with characteristics of fingerprint in the fingerprint base to obtain the orientation field of the fingerprint. This requires highly intensive human work, which is very complicated and time-consuming, and low in efficiency.
Moreover, as the conventional method for estimating the orientation field of the fingerprint only considers statistical information of fingerprint image blocks and the smoothness constraint of the fingerprint ridge, it has following disadvantages. Firstly, in the case of complicated background, it is difficult to distinguish whether an estimated initial orientation field relates to the background or to the fingerprint image if only the statistical information of the fingerprint image blocks is considered. Secondly, although the smoothness of an estimated orientation field can be ensured, the correctness of the estimated orientation field cannot be ensured if only the smoothness constraint of the fingerprint ridge is considered.